# onnx/tutorials

Tutorials for creating and using ONNX models

Repository: https://github.com/onnx/tutorials
Canonical: https://ross.abutalabs.com/products/onnx-tutorials
Language: Jupyter Notebook
License: Apache-2.0
License Family: permissive
Last push: 2026-06-21T05:13:13+00:00

## Health v2 (maintenance only)
Score: 72/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 88, release rhythm 35, longevity 100
- inputs: {"age_days": 3213, "days_push": 73, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 3708, forks 657 (observed 2026-08-28T04:08:15.108960+00:00)

## What it is
A collection of Jupyter Notebook tutorials for creating, converting, and using ONNX (Open Neural Network Exchange) machine learning models. It covers exporting models from frameworks like PyTorch, TensorFlow, Keras, scikit-learn, and more into the ONNX format.

## Use cases
- convert a pytorch model to onnx
- export a tensorflow keras model to onnx format
- learn how to run onnx models for inference
- convert scikit-learn models to onnx
- get started with the onnx model format
- deploy models across frameworks using onnx

## When to choose
- you need step-by-step notebooks for exporting models from various ML frameworks to ONNX
- you want to learn ONNX model interoperability across PyTorch, TensorFlow, scikit-learn and others
- you are looking for official ONNX conversion examples and best practices

## When to avoid
- you need the ONNX runtime or format specification itself rather than tutorials
- you want pre-trained ONNX models instead of conversion guides (see the ONNX Model Zoo)
- you need production tooling rather than educational notebooks

## Facets
- artifact type: learning-resource
- maturity: active
- function: machine-learning, documentation, developer-tools
- domain: machine-learning, tutorials, deep-learning, developer-tools
- platform: python, cross-platform
- tags: onnx, jupyter-notebooks, model-conversion, model-export, interoperability

## Member repositories
- onnx/tutorials (main) score 72

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:08:15.108960+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-29T18:30:57.916924+00:00, confidence not recorded.
  - readme: https://github.com/onnx/tutorials (fetched 2026-08-28T04:08:15.108960+00:00, sha 286c21304980)
- Data as of 2026-08-30T08:39:29.467469+00:00.